code: def _binary_op(cls, func, arg0, arg1):
      if not (
          isinstance(arg0, (cls, int, float))
          and isinstance(arg1, (cls, int, float))
      ):
          return NotImplemented
      v0, v1 = arg0, arg1
      if isinstance(arg0, cls):
          v0 = arg0.__values
      if isinstance(arg1, cls):
          v1 = arg1.__values
      # assert arg0.shape == arg1.shape
      if isinstance(arg0, cls) and isinstance(arg1, cls):
          msg = f"arg0 and arg1 need to have the same sparsity pattern in {func} (for now)"
          if not arg0.__row_offsets.shape == arg1.__row_offsets.shape:
              raise NotImplementedError(msg)
          if (
              not arg0.__column_indices.shape
              == arg1.__column_indices.shape
          ):
              raise NotImplementedError(msg)
          if not arg0.__values.shape == arg1.__values.shape:
              raise NotImplementedError(msg)
          # TODO this is not always true, but is a fast approximation for now
          if arg0.__row_offsets is not arg1.__row_offsets:
              raise NotImplementedError(msg)
          if arg0.__column_indices is not arg1.__column_indices:
              raise NotImplementedError(msg)
      out = func(v0, v1)
      return cls._wrap(
          arg0.shape,
          out,
          arg0.__row_indices,
          arg0.__row_offsets,
          arg0.__column_indices,
          arg0.__transp_info,
      )
  
imports: import torch
  from xformers.ops import masked_matmul
  from xformers.sparse import _csr_ops
  from xformers.sparse.utils import _csr_to_coo, _dense3d_to_sparse, _diffsort, _get_transpose_info, _transpose_with_info
label_desc: Use image node_id 20 for calling the SparseCSRTensor obj's underlying member method code with example usage: obj._binary_op(cls, func, arg0, arg1) and returns: cls, NotImplemented